Evolving chaotic neural systems for time series prediction
- Authors
- Lee, D.-W.; Sim, K.-B.
- Issue Date
- Jul-1999
- Publisher
- IEEE Computer Society
- Citation
- Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, v.1, pp 310 - 316
- Pages
- 7
- Journal Title
- Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999
- Volume
- 1
- Start Page
- 310
- End Page
- 316
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56579
- DOI
- 10.1109/CEC.1999.781941
- ISSN
- 0000-0000
- Abstract
- We present a new type of neural architecture consisting of chaotic neurons and apply it to the prediction of chaotic time series signals. To evolve chaotic neural systems, we use cellular automata whose production rules are evolved based on a DNA coding method. The structure of networks are appropriate for learning nonlinear, chaotic, and nonstationary systems. In order to verify their effectiveness, we apply the evolutionary chaotic neural systems to one-step ahead prediction of Mackey-Glass time series data. © 1999 IEEE.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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